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Record W2962719951 · doi:10.1126/science.aax3878

Thermal unequilibrium of strained black CsPbI <sub>3</sub> thin films

2019· article· en· W2962719951 on OpenAlexaff
Julian A. Steele, Handong Jin, Iurii Dovgaliuk, Robert Berger, Tom Braeckevelt, Haifeng Yuan, Cristina Martín, Eduardo Solano, Kurt Lejaeghere, Sven M. J. Rogge, Charlotte Notebaert, Wouter Vandezande, Kris P. F. Janssen, Bart Goderis, Elke Debroye, Ya‐Kun Wang, Yitong Dong, Dongxin Ma, Makhsud I. Saidaminov, Hairen Tan, Zheng‐Hong Lu, Vadim Dyadkin, Dmitry Chernyshov, Véronique Van Speybroeck, Edward H. Sargent, Johan Hofkens, Maarten B. J. Roeffaers

Bibliographic record

VenueScience · 2019
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsUniversity of Toronto
FundersKU LeuvenFonds Wetenschappelijk OnderzoekVlaamse regeringUniversiteit GentEuropean CommissionBelgian Federal Science Policy OfficeVlaams Supercomputer Centrum
KeywordsMaterials scienceAnnealing (glass)Thin filmPerovskite (structure)DiodeThermalScatteringOpticsCrystallographyOptoelectronicsComposite materialChemistryNanotechnologyThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Strain-stabilized perovskites The perovskite materials used for solar cells and light-emitting diodes (which are black in color) are generally less stable at room temperature than the electronically inactive nonperovskite phases (which are yellow in color). Steele et al. show that for CsPbI 3 , strain induced in a thin film after annealing the material to 330°C and then rapidly cooling it to room temperature kinetically trapped the black phase. Grazing-incidence wide-angle x-ray scattering revealed the crystal distortions and texture formation created by interfacial strain. Science , this issue p. 679

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.200
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations645
Published2019
Admission routes1
Has abstractyes

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